Fully or highly automated driving systems are designed to operate a vehicle on the road without driver interaction or other external control, for example, self-driving vehicles or autonomous vehicles. Autonomous vehicles are thus configured to make driving decisions in a manner consistent with manual control. These driving decisions can become complicated, consuming processing power in complex situations such as at intersections. This is especially true for driving decisions at yield scenarios, that is, where the autonomous vehicle must determine whether to proceed into an intersection with caution to avoid neighboring vehicles or to stop and wait, i.e. yield, until any neighboring vehicles have cleared the intersection.
Prior art driving systems include means for dynamically generating trajectories to navigate an intersection and for sectioning an intersection into a grid of segments to determine when vehicles should occupy specific segments of the intersection. In order to provide more stable driving behavior at an intersection while taking into account both the complexity of the traffic environment and the accuracy of information captured by the automated driving system about the traffic environment, the autonomous vehicle should be configured to differentiate between instances where minimum environment information is needed and where detailed environment information is needed to safely implement autonomous decision making at the intersection.